{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 克里金插值算法\n",
    "克里金插值（Kriging）是一种基于统计学的空间插值方法，广泛应用于地理信息系统、地质统计、环境科学等领域。它不仅能预测未知点的值，还能提供预测的误差估计。\n",
    "\n",
    "## 1、克里金插值的本质\n",
    "克里金假设空间数据具有**空间自相关性**（即距离越近的点相关性越强），并通过变差函数（Variogram）量化这种关系。其本质是**最优线性无偏估计（BLUE）**，在考虑数据空间结构的同时最小化预测误差。简单说：\n",
    "\n",
    "1. 线性：预测值是已知点的加权平均\n",
    "$$\n",
    "\\hat{Z}(x_0) = \\sum_{i=1}^{n} \\lambda_i Z(x_i)\n",
    "$$\n",
    "\n",
    "2. 无偏：无偏：要求权重和$\\sum_{i=1}^{n} \\lambda_i = 1$（保证预测值不会系统偏高或偏低）\n",
    "\n",
    "3. 最优：通过最小化预测方差$\\sigma^2$来确定权重$\\lambda_i$\n",
    "## 2、关键概念分步详解\n",
    "### 2.1变差函数\n",
    "#### 2.1.1变差函数的定义\n",
    "核心问题：如何量化\"距离越近的点越相似\"这一规律？\n",
    "\n",
    "解决方法：用变差函数（Variogram）描述数据差异随距离的变化。\n",
    "- **实验变差函数计算**(Empirical Variogram)\n",
    "    $$\n",
    "    \\gamma(h) = \\frac{1}{2N(h)} \\sum_{i=1}^{N(h)} \\left[ Z(x_i) - Z(x_i + h) \\right]^2\n",
    "    $$\n",
    "    - h：两点间的距离（滞后距离）\n",
    "    - N(h)：所有间距为 h的点对数量\n",
    "    - $Z(x_i)$：位置 $x_i$处的观测值\n",
    "\n",
    "    示例：假设有3对点间距为 h=2，它们的值差分别为 3,4,5，则：\n",
    "    $$\n",
    "    \\gamma(2) = \\frac{1}{2 \\times 3} \\left( 3^2 + 4^2 + 5^2 \\right) = \\frac{50}{6} \\approx 8.33\n",
    "    $$\n",
    "- **理论变差模型拟合**（常用模型）:\n",
    "\n",
    "| **模型名称** |                                                                                 **公式**                                                                                  |\n",
    "|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n",
    "|   球状模型   | $\\gamma(h) =  \\begin{cases}  c_0 + c \\left( \\frac{3h}{2a} - \\frac{h^3}{2a^3} \\right) & \\text{（当 } h \\leq a \\text{）} \\\\ c_0 + c & \\text{（当 } h > a \\text{）} \\end{cases}$ |\n",
    "|   指数模型   |                                                           $\\gamma(h) = c_0 + c \\left( 1 - e^{-3h/a} \\right)$                                                            |\n",
    "|   高斯模型   |                                                         $\\gamma(h) = c_0 + c \\left( 1 - e^{-3h^2/a^2} \\right)$                                                          |\n",
    "\n",
    "**参数解释**：\n",
    "\n",
    "- *c*0：块金效应（Nugget）—— 微观尺度的随机噪声\n",
    "- *c*：基台值（Sill）—— 变差函数的最大值\n",
    "- *a*：变程（Range）—— 自相关性消失的距离\n",
    "\n",
    "**图示**：\n",
    "\n",
    "```tex\n",
    "γ(h)\n",
    " |        /--------------\n",
    " |       /               (Sill c+c0)\n",
    " |      /\n",
    " |-----/----------------- (Nugget c0)\n",
    " |    /\n",
    " |___/___________________ h\n",
    "      (Range a)\n",
    "```\n",
    "#### 2.1.2变差函数的作用与意义\n",
    "- 空间自相关性分析：揭示数据是否随距离增加而变异性增强（如矿产品位、温度等）。\n",
    "- 指导模型选择：为拟合理论变差函数（如指数型、高斯型）提供依据，从而用于克里金权重计算。\n",
    "- 检测各向异性：若不同方向上的变差函数形态不同，可能需调整模型以考虑方向性影响。\n",
    "- 关键特征：\n",
    "    - 基台值（Sill）：变差趋于稳定的值，表示总变异性。\n",
    "    - 变程（Range）：变差达到基台值时的距离，反映自相关范围。\n",
    "    - 块金效应（Nugget）：h→0时的非零变差，表征测量误差或微观变异。\n",
    "\n",
    "\n",
    "## 3、完整算法流程\n",
    "\n",
    "### 案例：预测某区域地下水位（单位：米）\n",
    "\n",
    "已知4个观测点数据：\n",
    "\n",
    "| 点号 | 坐标 *x* | 坐标 *y* | 水位 *z* |\n",
    "| :--: | :------: | :------: | :------: |\n",
    "|  1   |    1     |    2     |   10.2   |\n",
    "|  2   |    3     |    1     |   11.5   |\n",
    "|  3   |    2     |    4     |   9.8    |\n",
    "|  4   |    4     |    3     |   12.1   |\n",
    "\n",
    "**目标**：预测点 $x_0$=(2.5,2.5)处的水位。\n",
    "\n",
    "#### 步骤1：计算实验变差函数\n",
    "\n",
    "假设我们已计算出变差函数为指数模型：$\\gamma(h) = 0.5 + 2.0 \\left( 1 - e^{-3h/4} \\right)$\n",
    "\n",
    "（参数：$c_0$=0.5, *c*=2.0, *a*=4）\n",
    "\n",
    "#### 步骤2：构建克里金方程组\n",
    "\n",
    "计算各点与 *x*0的距离：\n",
    "\n",
    "- $d(x_1, x_0) = \\sqrt{(1 - 2.5)^2 + (2 - 2.5)^2} \\approx 1.58$\n",
    "- $d(x_2, x_0) \\approx 1.58$\n",
    "- $d(x_3, x_0) \\approx 1.58$\n",
    "- $d(x_4, x_0) \\approx 1.58$\n",
    "\n",
    "查变差函数得：$\\gamma(1.58) = 0.5 + 2.0 \\left( 1 - e^{-3 \\times 1.58 / 4} \\right) \\approx 1.42$\n",
    "\n",
    "同理计算所有$\\gamma(x_i, x_j)$，得到矩阵方程：\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "0      & 1.42   & 1.42   & 1.42   & 1      \\\\\n",
    "1.42   & 0      & 1.42   & 1.42   & 1      \\\\\n",
    "1.42   & 1.42   & 0      & 1.42   & 1      \\\\\n",
    "1.42   & 1.42   & 1.42   & 0      & 1      \\\\\n",
    "1      & 1      & 1      & 1      & 0      \\\\\n",
    "\\end{bmatrix}\n",
    "\\quad\n",
    "\\begin{bmatrix}\n",
    "\\lambda_1 \\\\ \\lambda_2 \\\\ \\lambda_3 \\\\ \\lambda_4 \\\\ \\mu\n",
    "\\end{bmatrix}\n",
    "=\n",
    "\\begin{bmatrix}\n",
    "1.42 \\\\ 1.42 \\\\ 1.42 \\\\ 1.42 \\\\ 1\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "#### 步骤4：计算预测值\n",
    "\n",
    "$\\hat{z}(x_0) = 0.25 \\times 10.2 + 0.25 \\times 11.5 + 0.25 \\times 9.8 + 0.25 \\times 12.1 = 10.9 \\text{ 米}$\n",
    "\n",
    "#### 步骤5：计算预测方差\n",
    "\n",
    "$\\sigma^2 = 0.25 \\times 1.42 \\times 4 + (-0.42) = 1.0$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "id": "d8984a06d91e02af"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.113251Z",
     "start_time": "2025-08-11T10:37:42.109253Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from pykrige.ok import OrdinaryKriging\n",
    "import matplotlib.pyplot as plt"
   ],
   "id": "156591402281283d",
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.134388Z",
     "start_time": "2025-08-11T10:37:42.130126Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 输入数据（示例）\n",
    "x = np.array([1, 3, 2, 4])  # x坐标\n",
    "y = np.array([2, 1, 4, 3])  # y坐标\n",
    "z = np.array([10.2, 11.5, 9.8, 12.1])  # 观测值"
   ],
   "id": "8cbdd121f71942b2",
   "outputs": [],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.149705Z",
     "start_time": "2025-08-11T10:37:42.145706Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 普通克里金\n",
    "OK = OrdinaryKriging(\n",
    "    x, y, z,\n",
    "    variogram_model='exponential',\n",
    "    variogram_parameters={'nugget': 0.5, 'sill': 2.0, 'range': 4}\n",
    ")"
   ],
   "id": "44a0a44e7daff9fe",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.293594Z",
     "start_time": "2025-08-11T10:37:42.159389Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 预测点 (2.5, 2.5)\n",
    "z_pred, sigma = OK.execute('points', [2.5], [2.5])\n",
    "print(f\"预测值: {z_pred[0]:.1f} 米, 标准差: {np.sqrt(sigma[0]):.1f} 米\")\n",
    "plt.scatter(x, y, c=z, s=100, cmap='coolwarm')\n",
    "plt.scatter(2.5, 2.5, c='red', s=100)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ],
   "id": "81802a4efb9f92fe",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值: 10.9 米, 标准差: 1.3 米\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 克里金算法实战-随机数据\n",
    "\n",
    "https://yuanbao.tencent.com/bot/app/share/chat/2nsm9sgImWwi"
   ],
   "id": "40044446a2dc8a84"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.308201Z",
     "start_time": "2025-08-11T10:37:42.304780Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pykrige.ok import OrdinaryKriging"
   ],
   "id": "38ba9591d2e960e8",
   "outputs": [],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 1、生成模拟 数据\n",
    "随机生成10个空间点（坐标范围[0,10]）及其对应的观测值（假设为某地温度值，单位℃）。"
   ],
   "id": "cd0d3739191cac4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.327720Z",
     "start_time": "2025-08-11T10:37:42.323718Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 设置随机数种子（确保每次生成的数据相同）\n",
    "np.random.seed(42)\n",
    "n_points = 100\n",
    "x = np.random.uniform(0, 10, n_points)\n",
    "y = np.random.uniform(0, 10, n_points)\n",
    "z = np.random.normal(15, 25, n_points)  # 观测值：温度范围15-25度"
   ],
   "id": "893eeae164b6023f",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.347424Z",
     "start_time": "2025-08-11T10:37:42.342562Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ],
   "id": "7af04cbbcb7d9960",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.74540119 9.50714306 7.31993942 5.98658484 1.5601864  1.5599452\n",
      " 0.58083612 8.66176146 6.01115012 7.08072578 0.20584494 9.69909852\n",
      " 8.32442641 2.12339111 1.81824967 1.8340451  3.04242243 5.24756432\n",
      " 4.31945019 2.9122914  6.11852895 1.39493861 2.92144649 3.66361843\n",
      " 4.56069984 7.85175961 1.99673782 5.14234438 5.92414569 0.46450413\n",
      " 6.07544852 1.70524124 0.65051593 9.48885537 9.65632033 8.08397348\n",
      " 3.04613769 0.97672114 6.84233027 4.40152494 1.22038235 4.9517691\n",
      " 0.34388521 9.09320402 2.58779982 6.62522284 3.11711076 5.20068021\n",
      " 5.46710279 1.84854456 9.69584628 7.75132823 9.39498942 8.9482735\n",
      " 5.97899979 9.21874235 0.88492502 1.95982862 0.45227289 3.25330331\n",
      " 3.8867729  2.71349032 8.28737509 3.56753327 2.8093451  5.42696083\n",
      " 1.40924225 8.02196981 0.74550644 9.86886937 7.72244769 1.98715682\n",
      " 0.05522117 8.15461428 7.06857344 7.29007168 7.71270347 0.74044652\n",
      " 3.58465729 1.1586906  8.63103426 6.23298127 3.30898025 0.6355835\n",
      " 3.10982322 3.25183322 7.29606178 6.37557471 8.87212743 4.72214925\n",
      " 1.19594246 7.13244787 7.60785049 5.61277198 7.7096718  4.93795596\n",
      " 5.22732829 4.27541018 0.25419127 1.07891427]\n",
      "[0.31429186 6.36410411 3.14355981 5.08570691 9.07566474 2.49292229\n",
      " 4.10382923 7.55551139 2.28798165 0.7697991  2.89751453 1.61221287\n",
      " 9.29697652 8.0812038  6.33403757 8.7146059  8.03672077 1.86570059\n",
      " 8.92558998 5.39342242 8.07440155 8.960913   3.18003475 1.10051925\n",
      " 2.27935163 4.27107789 8.18014766 8.60730583 0.06952131 5.10747303\n",
      " 4.17411003 2.2210781  1.19865367 3.37615171 9.42909704 3.23202932\n",
      " 5.18790622 7.03018959 3.63629602 9.71782083 9.62447295 2.51782296\n",
      " 4.97248506 3.0087831  2.84840494 0.36886947 6.09564334 5.02679023\n",
      " 0.51478751 2.78646464 9.08265886 2.39561891 1.44894872 4.8945276\n",
      " 9.85650454 2.42055272 6.72135547 7.61619615 2.37637544 7.28216349\n",
      " 3.67783133 6.32305831 6.33529711 5.35774684 0.9028977  8.35302496\n",
      " 3.20780065 1.8651851  0.40775142 5.90892943 6.77564362 0.16587829\n",
      " 5.12093058 2.26495775 6.4517279  1.74366429 6.90937738 3.86735346\n",
      " 9.36729989 1.37520944 3.41066351 1.13473521 9.24693618 8.77339353\n",
      " 2.57941628 6.59984046 8.172222   5.55200812 5.29650578 2.41852291\n",
      " 0.93102768 8.97215758 9.00418057 6.33101457 3.39029791 3.49209575\n",
      " 7.25955679 8.9711026  8.87086424 7.79875546]\n",
      "[ -2.00061804  20.80634243  22.32681183  -2.85878545  61.64436278\n",
      "  26.84582302 -14.78258743  31.41384022  -9.36704176  34.67711509\n",
      "  43.96488948  -5.51705796  39.08440323  25.31952317  35.551504\n",
      "  62.41982457   8.8652971   -3.84340411  -7.23786074  -5.39525712\n",
      "  13.07245726  23.52879937  21.91726998  35.67958123  15.3250473\n",
      "  51.33835193   8.38357917  83.00422916  30.64168369  -6.42893891\n",
      " -11.77231245  27.06181038   9.41343037  32.85001235  26.83094061\n",
      "  13.17927718  -6.16984295 -22.87118062   3.8371262   36.40996986\n",
      "  20.3523436  -16.14346947  19.32952315  24.63293449  -7.09643591\n",
      "  18.84312765  16.45521796 -13.57425745  23.94468401  29.01961316\n",
      "  42.07628108  41.3450513  -19.4417342   -8.445626    27.87588168\n",
      "  27.84464877  27.87619216 111.31828727  29.27226277  43.389141\n",
      "  38.85004409  31.28478128   7.11826888  33.97423051  -4.32063036\n",
      "   9.07953483   2.8659113   17.04685348  72.86646417 -31.68162981\n",
      "  32.15650476 -25.31789678   3.20170336  42.22376492  16.60700048\n",
      " -11.94361945  -2.88259273  31.98994372  -3.25916579  20.41146474\n",
      "  16.139296    -1.29000869  68.59860223  30.84797556 -35.62856467\n",
      "  19.66135787  -1.54466162  36.31083337  -4.81301846  12.13158896\n",
      "  27.62468197  36.64387985 -15.00741018   6.6374691    3.12636722\n",
      "  -1.33323081  59.13635601  25.12454277 -16.52209886  37.94654868]\n"
     ]
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.365792Z",
     "start_time": "2025-08-11T10:37:42.361286Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 解决中文乱码的问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置黑体（Windows常用）\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题"
   ],
   "id": "61096d64a488de8",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.488241Z",
     "start_time": "2025-08-11T10:37:42.377664Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可视化\n",
    "plt.scatter(x, y, c=z, s=100, cmap='coolwarm')\n",
    "# plt.scatter(x, y, c=z, cmap='jet', s=50, edgecolor='k')\n",
    "plt.colorbar(label='温度 (度)')\n",
    "plt.title('模拟数据')\n",
    "plt.show()"
   ],
   "id": "a63f92a93421c0ea",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 二、计算实验变差函数、拟合理论变差模型\n",
    "实验变差函数用于量化空间自相关性。使用skgstat库计算"
   ],
   "id": "cc0555731d7de3b4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T11:21:58.156052Z",
     "start_time": "2025-08-11T11:21:57.834953Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from skgstat import Variogram\n",
    "# 计算实验变差函数\n",
    "V = Variogram(\n",
    "    coordinates=np.column_stack((x, y)),\n",
    "    values=z,\n",
    "    bin_func='even',  # 等间距划分距离区间\n",
    "    n_lags=10,  # 分成10个距离区间\n",
    "    maxlag=7,  # 最大滞后距离调整为7\n",
    "    use_nugget=True,  # 启用块金效应拟合\n",
    "    model='exponential'\n",
    ")\n",
    "# 拟合模型\n",
    "V.fit()\n",
    "\n",
    "# 绘制变差函数\n",
    "plt.figure(figsize=(8,6))\n",
    "V.plot()\n",
    "plt.title('变差函数')\n",
    "plt.grid(alpha = 0.3)\n",
    "plt.show()\n",
    "\n",
    "# 输出关键参数\n",
    "print(f\"变程（Range）估计值：{V.cof[0]:.2f}\")\n",
    "print(f\"基台值（Sill）估计值: {V.cof[1]:.2f}\")\n",
    "print(f\"块金效应（Nugget）估计值：{V.cof[2]:.2f}\")\n"
   ],
   "id": "c1d7e30397230743",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "变程（Range）估计值：7.00\n",
      "基台值（Sill）估计值: 47.33\n",
      "块金效应（Nugget）估计值：581.08\n"
     ]
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**输出数据解读**：\n",
    "\n",
    "1. **变程（Range）估计值：7.00**\n",
    "   - 这表示在空间距离达到 **7.0 单位**时，数据点的空间自相关性显著减弱。\n",
    "   - *解读*：相邻点在此距离内存在相关性，超过此距离则相关性可忽略。\n",
    "2. **基台值（Sill）估计值：47.33**\n",
    "   - 代表数据的 **结构化空间变异性**（由空间位置引起的变异部分）。\n",
    "   - *解读*：空间相关性的贡献为 47.33（单位平方）。\n",
    "3. **块金效应（Nugget）估计值：581.08**\n",
    "   - 反映 **微观尺度变异**或 **测量误差**（距离趋近于 0 时的变差值）。\n",
    "   - *解读*：块金值远高于基台值（581.08 vs 47.33），表明：\n",
    "     - 数据中可能存在大量噪声或异常值。\n",
    "     - 空间结构性较弱（随机变异主导）。\n",
    "\n",
    "\n",
    "> 当前结果表明数据可能存在 高噪声 或 弱空间相关性\n"
   ],
   "id": "e764c07d661495c8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T10:37:42.740161Z",
     "start_time": "2025-08-11T10:37:42.736176Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"参数顺序:\", V.cof)\n",
    "print(V.describe())"
   ],
   "id": "47c1c91a27d3ccf2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数顺序: [  6.99957723  47.33124293 581.08212366]\n",
      "{'model': 'exponential', 'estimator': 'matheron', 'dist_func': 'euclidean', 'normalized_effective_range': 48.99408140538213, 'normalized_sill': 31646.86622547701, 'normalized_nugget': 388526.20583323267, 'effective_range': 6.999577230474861, 'sill': 47.33124293131826, 'nugget': 581.0821236597468, 'params': {'estimator': 'matheron', 'model': 'exponential', 'dist_func': 'euclidean', 'bin_func': 'even', 'normalize': False, 'fit_method': 'trf', 'fit_sigma': None, 'use_nugget': True, 'maxlag': 7, 'n_lags': 10, 'verbose': False}, 'kwargs': {}}\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 三、执行克里金插值\n",
    "预测整个区域的温度分布："
   ],
   "id": "3bb9220f7db74060"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T11:32:43.096649Z",
     "start_time": "2025-08-11T11:32:42.864491Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 生成预测网格\n",
    "grid_x=np.linspace(0, 10, 100)\n",
    "grid_y=np.linspace(0, 10, 100)\n",
    "\n",
    "# 普通克里金插值\n",
    "OK = OrdinaryKriging(\n",
    "    x, y, z,\n",
    "    variogram_model='exponential',\n",
    "    variogram_parameters=list(V.cof)\n",
    ")\n",
    "# 执行插值\n",
    "z_pred, sigma = OK.execute('grid', grid_x, grid_y)\n",
    "\n",
    "# 预测结果\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.imshow(z_pred, cmap='coolwarm', origin='lower', extent=[0, 10, 0, 10])\n",
    "plt.colorbar(label='温度 (度)')\n",
    "plt.title('克里金插值结果')\n",
    "plt.show()\n",
    "\n"
   ],
   "id": "b53dd19cce156867",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-11T11:33:22.561075Z",
     "start_time": "2025-08-11T11:33:22.484686Z"
    }
   },
   "cell_type": "code",
   "source": [
    "z_new, sigma_new = OK.execute('points', [5.5], [5.5])\n",
    "print(f\"预测点(5.5,5.5)的温度: {z_new[0]:.2f}°C ± {np.sqrt(sigma_new[0]):.2f}°C\")"
   ],
   "id": "d787ac6a0087b039",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测点(5.5,5.5)的温度: -12.05°C ± 24.60°C\n"
     ]
    }
   ],
   "execution_count": 47
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "e0fe199db6bad210"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "af5c75cbc5de12e2"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "8828a5ac0cb86c9f"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
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